FIG. 1 depicts a representational diagram of a user 140 in transit from a source location 110 to a destination location 120, in which user 140 carries a mobile communications device 145 while in transit. User 140 might use any mode of travel to get from source 110 to destination 120, such as walking, automobile, train, airplane, etc. In the prior art, if user 140 wishes to estimate his/her time-of-arrival at destination 120, he/she can consider, in concert with the current time, information such as (i) how long it has normally taken in the past to get to destination 120 from user 140's current position; (ii) user 140's average speed; (iii) the distance remaining to destination 120; (iv) traffic; (v) weather; (vi) expected traffic for the remainder of the trip; and (vii) expected weather for the remainder of the trip.
User 140 might estimate item (i) through (vii) above, respectively, based on: (i) memory; (ii) an odometer, speedometer, or global positioning system (GPS), as is well understood in the art; (iii) an odometer, GPS, or observational approximation (e.g., based on a landmark, road sign, etc.); (iv/v) observation; (vi/vii) forecasts received via radio, mobile communications device 145, etc.
It is apparent, therefore, that estimating the time-of-arrival can be an inconvenient and cumbersome task, particularly if user 140 wishes to periodically recalculate the time-of-arrival as the trip progresses. Furthermore, in some instances at least one of the data items mentioned above might not be available, for example, due to: poor radio reception; commercial radio weather and traffic reports provided only sporadically; poor signal quality for mobile communications device 145; no data capability for mobile communications device 145; inability to read odometer/speedometer (e.g., chartered-bus passenger, etc.)
In addition, in some situations it might be dangerous to perform such time-of-arrival estimates; for example, an automobile driver may not pay sufficient attention to the road while he/she (i) is performing mental calculations, or (ii) fumbling with the radio, navigation system, or mobile communications device 145 to receive traffic and weather information. Finally, it might be desirable for a user to receive metrics in addition to the expected time-of-arrival, such as the earliest (best-case) time-of-arrival, or a pessimistic time-or-arrival (e.g., mean plus one standard deviation, etc.). Such additional metrics might also be inconvenient and/or difficult to estimate mentally, if not more so than the expected time-of-arrival. Therefore, the need exists for an automated method that overcomes these disadvantages.